The electroencephalogram (EEG) is a record of the electrical voltage signal recorded on the scalp. The EEG is in reality the summated electrical activity of the billions of neurons which make up the brain. In order to study the electrical activity of the brain, a simplified model may be utilized which considers the EEG to be produced by a relatively small number of current sources located in the brain, which is considered to be a volume conductor. The problem of identifying unknown internal current sources within a volume conductor from a known set of voltage measurements on the surface of the volume is called the “inverse problem.” (The “forward problem” is the process of estimating voltages on a surface given known sources of electrical activity within a volume.) There are a number of methods for providing solutions to the inverse problem used in both cardiology (linking the electrocardiogram or ECG with the heart) and neurology (linking EEG with the brain). Because the inverse solution is not unique, approximations are estimated using a set of assumptions and constraints regarding the sources. For purposes of this invention, we shall use the term “Inverse Method” as a generic name for an analytical tool that takes a large set of voltage measurements (spatially distributed around the source organ) and estimates the source properties. Therefore, given a set of EEG signals collected simultaneously from the scalp of a subject, an Inverse Method can be used to calculate the underlying cerebral electrical activity which generated the EEG data, given a set of mathematical constraints. One commonly used Inverse Method is Low Resolution Brain Electromagnetic Tomography (LORETA) as described in “Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain,” Pascual-Marqui R D, Michel C M, Lehmann D. International Journal of Psychophysiology 1994, 18:49-65.
Clinicians often image the brain, focusing on particular regions of interest to aid in the diagnosis of particular abnormalities. A wide variety of algorithms and mathematical models have been developed to derive images of brain activity from EEG signals recorded from scalp electrodes (see, for example, U.S. Pat. Nos. 4,862,359 and 4,736,751). EEG imaging of the brain, as it is generally implemented in research studies, typically requires large, multi-channel, full-head electrode montages which are impractical in many clinical settings. Such techniques typically use at least 19 channels of EEG data, and more usually 24 channels. Without these large electrode sets, the spatial detail in the resultant image becomes poor and provides little information to the clinician.
The clinical application of such techniques is limited, however, in part due to the time-consuming and technically difficult need to apply 19-24 electrodes over the entire scalp of a patient. In addition, EEG recording devices which simultaneously collect large numbers of channels are expensive and complex, due to the need to provide a large number of high-fidelity channels. For this reason, it would be desirable to obtain EEG images using smaller data sets without the loss of spatial resolution, the usual trade-off for smaller number of channels. There are various reported methods of improving spatial detail of EEG-derived images based on anthropomorphic data as well as measurements of electrodes position, scalp and skull thickness, as well as brain shape; see for example U.S. Pat. No. 5,568,816 issued to Gevins et al. entitled “EEG Deblurring Method and System for Improved Spatial Detail” and U.S. Pat. No. 5,331,970 issued to Gevins et al. for “EEG Spatial Enhancement Method & System”. However, in the case in which an inverse solution is applied to a limited set of EEG channels (e.g., 4 channels), these methods cannot provide sufficient spatial resolution to be useful in a clinical application.
It is therefore an object of the present invention to provide EEG-derived images of comparable resolution in a particular region using a small subset of EEG channels to those derived using a full set of EEG channels.